The course covers concepts in probabilistic machine learning: independence, conditional independence, mixture models, EM algorithm, Bayesian networks, latent linear models, and algorithms for exact and approximate inference, with an emphasis on variational inference, which helps derive approximate inference algorithms for complex models
active 2024-02-17 → 2024-09-06 (UTC)
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